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Publicações

Publicações por Francisco Miguel Cruz

2015

CumuloNimbo: A Cloud Scalable Multi-tier SQL Database

Autores
Peris, RJ; Martínez, MP; Kemme, B; Brondino, I; Pereira, JO; Vilaça, R; Cruz, F; Oliveira, R; Ahmad, MY;

Publicação
IEEE Data Eng. Bull.

Abstract

2016

Resource Usage Prediction in Distributed Key-Value Datastores

Autores
Cruz, F; Maia, F; Matos, M; Oliveira, R; Paulo, J; Pereira, J; Vilaca, R;

Publicação
DISTRIBUTED APPLICATIONS AND INTEROPERABLE SYSTEMS, DAIS 2016

Abstract
In order to attain the promises of the Cloud Computing paradigm, systems need to be able to transparently adapt to environment changes. Such behavior benefits from the ability to predict those changes in order to handle them seamlessly. In this paper, we present a mechanism to accurately predict the resource usage of distributed key-value datastores. Our mechanism requires offline training but, in contrast with other approaches, it is sufficient to run it only once per hardware configuration and subsequently use it for online prediction of database performance under any circumstance. The mechanism accurately estimates the database resource usage for any request distribution with an average accuracy of 94 %, only by knowing two parameters: (i) cache hit ratio; and (ii) incoming throughput. Both input values can be observed in real time or synthesized for request allocation decisions. This novel approach is sufficiently simple and generic, while simultaneously being suitable for other practical applications.

2013

MeT

Autores
Cruz, F; Maia, F; Matos, M; Oliveira, R; Paulo, J; Pereira, J; Vilaça, R;

Publicação
Proceedings of the 8th ACM European Conference on Computer Systems - EuroSys '13

Abstract

2013

MeT: Workload aware elasticity for NoSQL

Autores
Cruz, F; Maia, F; Matos, M; Oliveira, R; Paulo, J; Pereira, J; Vilaca, R;

Publicação
Proceedings of the 8th ACM European Conference on Computer Systems, EuroSys 2013

Abstract
NoSQL databases manage the bulk of data produced by modern Web applications such as social networks. This stems from their ability to partition and spread data to all available nodes, allowing NoSQL systems to scale. Unfortunately, current solutions' scale out is oblivious to the underlying data access patterns, resulting in both highly skewed load across nodes and suboptimal node configurations. In this paper, we first show that judicious placement of HBase partitions taking into account data access patterns can improve overall throughput by 35%. Next, we go beyond current state of the art elastic systems limited to uninformed replica addition and removal by: i) reconfiguring existing replicas according to access patterns and ii) adding replicas specifically configured to the expected access pattern. MeT is a prototype for a Cloud-enabled framework that can be used alone or in conjunction with OpenStack for the automatic and heterogeneous reconfiguration of a HBase deployment. Our evaluation, conducted using the YCSB workload generator and a TPC-C workload, shows that MeT is able to i) autonomously achieve the performance of a manual configured cluster and ii) quickly reconfigure the cluster according to unpredicted workload changes. © 2013 ACM.

2013

An effective scalable SQL engine for NoSQL databases

Autores
Vilaca, R; Cruz, F; Pereira, J; Oliveira, R;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
NoSQL databases were initially devised to support a few concrete extreme scale applications. Since the specificity and scale of the target systems justified the investment of manually crafting application code their limited query and indexing capabilities were not a major impediment. However, with a considerable number of mature alternatives now available there is an increasing willingness to use NoSQL databases in a wider and more diverse spectrum of applications and, to most of them, hand-crafted query code is not an enticing trade-off. In this paper we address this shortcoming of current NoSQL databases with an effective approach for executing SQL queries while preserving their scalability and schema flexibility. We show how a full-fledged SQL engine can be integrated atop of HBase leading to an ANSI SQL compliant database. Under a standard TPC-C workload our prototype scales linearly with the number of nodes in the system and outperforms a NoSQL TPC-C implementation optimized for HBase. © 2013 IFIP International Federation for Information Processing.

2016

Towards Performance Prediction in Massive Scale Datastores

Autores
Cruz, F; Coelho, F; Oliveira, R;

Publicação
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE, VOL 1 (CLOSER)

Abstract
Buffer caching mechanisms are paramount to improve the performance of today's massive scale NoSQL databases. In this work, we show that in fact there is a direct and univocal relationship between the resource usage and the cache hit ratio in NoSQL databases. In addition, this relationship can be leveraged to build a mechanism that is able to estimate resource usage of the nodes composing the NoSQL cluster.

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